GuardIQ: A Post-Quantum Secure VIP Threat Detection and Monitoring Platform Using AI-Powered Intelligence and Biometric Authentication

Abstract

ABSTRACT In the contemporary digital landscape, high-profile individuals including celebrities, executives, political leaders, and public officials face unprecedented threats from online impersonation, sophisticated misinformation campaigns, AI-generated deepfakes, and fraudulent social media profiles. The convergence of generative artificial intelligence technologies and social media platforms has dramatically expanded the attack surface, enabling malicious actors to create synthetic identities, manipulate multimedia content, and spread false narratives with alarming ease and speed. Existing security solutions remain fragmented, requiring extensive manual intervention and lacking the capability for real-time monitoring and automated threat response, thereby leaving critical gaps in digital protection for vulnerable public figures. This research paper presents GuardIQ, an integrated, fully automated, end-to-end VIP Threat Detection and Monitoring Platform that combines post-quantum cryptography, multi-factor biometric authentication, artificial intelligence-powered threat detection, and blockchain-based evidence preservation. The platform architecture is built upon seven core pillars: quantum-secure biometric registration utilizing Kyber Key Encapsulation Mechanism (KEM), real-time threat detection engine monitoring multiple social media platforms, AI-powered content verification distinguishing authentic media from AI-generated deepfakes, automated fake profile detection comparing discovered accounts against registered handles, live analyzer for instant authenticity verification, immutable evidence collection using Web3 technologies, and unified dashboard providing comprehensive threat intelligence visualization. GuardIQ employs CRYSTALS-Kyber post-quantum cryptographic algorithms (Kyber512 for lightweight mobile endpoints and Kyber768/1024 for enterprise deployments) combined with AES-256-GCM symmetric encryption to ensure quantum-resistant data protection. The biometric registration module captures facial recognition data, voice patterns, gesture signatures, and official social media handles, all protected through quantum-safe encryption. Large Language Models (LLMs) integrated within the threat detection engine perform real-time classification of suspicious content, achieving 92-97% accuracy in identifying impersonation attempts, misinformation campaigns, and image misuse across platforms including Twitter, Facebook, Instagram, and LinkedIn. The AI content detection module leverages advanced deep learning architectures including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential pattern detection, and transformer-based models for multimedia authenticity verification. Experimental results demonstrate the system's capability to distinguish AI-generated content from authentic material with confidence scores exceeding 94%, providing early detection of deepfakes and synthetic media targeting VIP credibility. The fake profile detection algorithm analyzes multiple parameters including account creation timestamps, username patterns, biographical information, follower-to-following ratios, engagement metrics, and posting behavior patterns to identify fraudulent accounts with 89% precision. Evidence collection is facilitated through Web3-based blockchain infrastructure ensuring tamper-proof, immutable storage of all flagged incidents, suspicious posts, and detected impersonations. This cryptographically verifiable evidence chain supports legal proceedings and investigative actions by providing irrefutable proof of malicious activities. The unified dashboard aggregates threat intelligence from all modules, presenting real-time alerts, authenticity scores, risk assessments, and recommended remediation actions through intuitive visualizations requiring minimal manual oversight. Performance evaluation reveals that post-quantum TLS handshakes introduce only 5-10 milliseconds additional latency compared to classical TLS implementations, demonstrating practical feasibility for production deployment. The automated threat detection pipeline reduces incident response time by 72% compared to manual monitoring approaches, while the quantum-resistant encryption framework ensures long-term security against emerging quantum computing threats. System architecture supports horizontal scalability through microservices deployment, containerization using Docker and Kubernetes orchestration, and cloud-native infrastructure compatible with AWS, Azure, and Google Cloud Platform. This research addresses the urgent need for comprehensive digital protection solutions in an era where AI-generated content, quantum computing capabilities, and sophisticated social engineering attacks converge to create unprecedented risks for public figures. GuardIQ represents a paradigm shift from reactive security measures to proactive, automated threat intelligence platforms capable of defending high-profile individuals against modern digital adversaries while maintaining usability, scalability, and legal compliance. Keywords : VIP Protection, Post-Quantum Cryptography, Kyber KEM, Deepfake Detection, AI Content Verification, Biometric Authentication, Threat Intelligence, Social Media Monitoring, Blockchain Evidence, Web3 Security, Impersonation Detection, Misinformation Prevention, Large Language Models, Zero- Trust Architecture, Quantum-Safe Encryption, Identity Verification, Automated Security Response, Digital Reputation Management